Recent work reconsiders graph representation as a matrix factorization problem and unifies DeepWalk, LINE, node2vec, etc. into a matrix factorization task[1]}. This shared representation reconsiders the differences between the approaches to be differences in the transition probabilities for random walks over the graph. Still, the learned representations are not meaningful outside of their relative distance in a vector-space.
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